ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-9-813-2009On the diagnosis of climate sensitivity using observations of fluctuationsKirk-DavidoffD. B.11Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA0202200993813822This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from http://www.atmos-chem-phys.net/9/813/2009/acp-9-813-2009.htmlThe full text article is available as a PDF file from http://www.atmos-chem-phys.net/9/813/2009/acp-9-813-2009.pdf

It has been shown that lag-covariance based statistical measures,
suggested by the Fluctuation Dissipation Theorem (FDT), may allow
estimation of climate sensitivity in a climate model. Recently
Schwartz (2007) has used measures of the decay of autocorrelation
in a global surface temperature time series to estimate the real
world climate sensitivity. Here we use a simple climate model, and
analysis of archived coupled climate model output from the IPCC AR4
runs, for which the climate sensitivity is known, to test the
utility of this approach. Our analysis of these archived model
output data show that estimates of climate sensitivity derived from
century-long time scales typically grossly underestimate the models'
true climate sensitivity. We analyze the behavior of the simple
model with adjustable heat capacity in two surface layers, subject
to various stochastic forcings and for various climate
sensitivities, modulated by albedo and water vapor feedbacks. We use
our simple climate model to demonstrate:
<br><br>
1. that a much longer time series would be
required to accurately diagnose the earth's climate sensitivity than is
presently available
<br><br>
2. that for shorter time series there is a
systematic bias towards underpredicting climate sensitivity,
<br><br>
3. that
the addition of a second heat reservoir weakly coupled to the first
greatly reduces the decorrelation timescale of short temperature
time series produced by the model, aggravating the tendency to
underestimate climate sensitivity, and
<br><br>
4. that because of this it is
possible to have a selection of models in which the climate
sensitivity is inversely related to the decorrelation time scale, as
is true for the IPCC models.